Chapter 7_Case Study

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Macy Newman BAN 6093 Dr. Makins March 31, 2024 Chapter 7: Case Problem 2. Consumer Research Inc. Introduction This report analyzes credit card usage data from 50 consumers, provided by Consumer Research, Inc. Our goal is to identify consumer characteristics that predict annual credit card charges. We'll use descriptive statistics to summarize the data and regression analysis to explore relationships between income, household size (independent variables) and annual charges (dependent variable). The analysis will identify which variable is a better predictor and culminate in a multiple regression model for improved prediction. Finally, we'll discuss the model's limitations and suggest additional variables for future research. Descriptive Statistics: Data Summarization This section analyzes the income, household size, and credit card spending patterns within the sample population. The findings provide valuable insights into the characteristics of this group and can inform future decision-making strategies. Income Distribution  Mean Income: $43,480 represents the average income within the sample.  Range and Skewness: The data exhibits a wider range of income levels, indicating a diverse sample population. Furthermore, the right skewness suggests a higher concentration of individuals with lower incomes compared to those with higher incomes. Understanding this distribution can be crucial for tailoring marketing campaigns or product offerings to specific income brackets.

Household Size  Mean Household Size: The average household size in the sample is 3.42.  Range and Skewness: The data displays a narrower range for household size, suggesting relative stability in the number of individuals per household within the sample. This information can be helpful for understanding the consumption patterns and needs of typical households within the group. Credit Card Spending Patterns  Mean Amount Charged: The average amount charged on credit cards within the sample is $[3,964.06].  Range and Skewness: While spending patterns exhibit some variability, the distribution shows less skewness compared to income. This suggests a wider range of spending habits but without a significant concentration at extreme. Analyzing spending patterns can inform strategies for promoting specific products or services, loyalty programs, and overall customer engagement. The analysis reveals a sample population with varying income levels, relatively stable household sizes, and diverse credit card spending patterns. This information highlights the importance of considering these factors when developing marketing initiatives, product offerings, and customer service strategies. By understanding the demographics and spending behaviors of the target audience, businesses can tailor their approach to maximize customer satisfaction and achieve long-term success. Regression with Income:

The regression equation is: Annual credit card charges = 2,203.9996 + 40.4798 * Income ($1000) R-Squared (R^2) = 0.3981 The coefficient for income is statistically significant (p-value < 0.05), indicating that income has a significant impact on credit card charges. The Estimated Regression Equations Using Household Size as the Independent Variable The regression equation is: Annual Credit Card Charges = 2,581.941 + 404.128 *Household Size R-squared (R^2) = 0.5668 The coefficient for household size is statistically significant (p-value < 0.05), indicating that household size is a significant predictor of credit card charges. The R-squared value is higher for the regression with household size (0.5668) compared to the regression with income (0.3981). This indicates that household size explains a larger portion of the variance in credit card charges compared to income. Both income and household size have statistically significant coefficients, suggesting that both variables have a significant impact on credit card charges.

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Estimated Regression Equation The estimated regression equation with annual income and household size as independent variables is: Annual credit card charges = 1,304.9048 + 33.1330 Income ($1,000) + 356.2959 Household Size  The intercept term (1,304.9048) represents the estimated annual credit card charges when both income and household size are zero, which is not meaningful in this context.  For every $1,000 increase in annual income, the estimated annual credit card charges increase by $33.1330, holding household size constant.  For every additional person in the household, the estimated annual credit card charges increase by $356.2959, holding income constant. Both income and household size have statistically significant coefficients (p-values < 0.05), indicating that both variables are significant predictors of annual credit card charges. The R-squared value is 0.8256, indicating that 82.56% of the variance in annual credit card charges can be explained by the regression model with income and household size. Predicted Annual Credit Card Charge The predicted annual credit card charge for a three-person household with an annual income of $40,000 is: Annual credit card charges = 1,304.9048 + (33.1330 * 40) + (356.2959 * 3) = 3,699.1125 Recommendations

To further refine the model and gain a deeper understanding of annual credit card spending, incorporating additional variables is crucial. Here's why:  Financial Health: Credit score and debt-to-income ratio offer invaluable insights into an individual's financial health and risk tolerance. A high credit score might indicate a greater willingness to utilize credit cards for larger purchases, while a high debt-to- income ratio could suggest dependence on credit to meet basic needs.  Consumer Profile: Education level, age, and marital status paint a clearer picture of the consumer. Younger individuals might exhibit higher spending on entertainment or technology, while families with children may have different spending priorities. Understanding these factors allows for targeted marketing and product development.  Lifestyle and Location: Geographic location can influence spending habits due to cost- of-living variations. Similarly, employment status can impact income and spending patterns. Individuals with stable employment might have more disposable income to spend on credit cards compared to those unemployed or in precarious job situations. By incorporating these additional variables, the model can move beyond basic demographics to create a more comprehensive profile of the consumer. This detailed understanding allows for a more nuanced analysis of factors influencing annual credit card charges, leading to improved decision-making across various business areas. Conclusion The analysis of credit card usage data from 50 consumers identified income and household size as significant predictors of annual charges. While household size exhibited a stronger initial relationship (R² = 0.5668), the final model incorporating both variables yielded a substantially improved R² of 0.8256. However, limitations exist. The sample size and lack of demographic diversity may restrict generalizability. Future research should explore additional variables like credit score, demographics, and lifestyle factors to create a more comprehensive consumer profile and enhance the model's predictive power. This can inform targeted marketing, product development, and credit risk assessment, ultimately leading to better customer experiences and improved business outcomes.

Chapter 7_Case Study

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